Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Experts trace a congruent trend, pinpointed as originating around the 2010s (Grose, 2020) and only accelerating in the pandemic and its aftermaths: the rise of social media activity relating to parents’ performances of their substance abuse – what this paper defines as “#winemom culture” – with a broader social tendency, a general increase in “rates of high-risk drinking” that lead to such outcomes as “long-term health damage” and “dangers to family” (Macarthur, n.d.). I interrogate the ethics of moralizing against #winemom culture under COVID-19 culture and its aftermaths through exclusively quantitative metrics or surface-level analysis. As with anything coded according to the “momification of the Internet” (Dewey, 2015), such cultures are often disregarded, seen as superficial or in receipt of unchecked judgments. I trace the following question: What can #winemom culture reveal about how parents are processing and communicating within this moment? And begin from the premise that there are as-yet undetermined drivers motivating what appears to be a “zoning out” (Heyes, 2020) in the mediation of #winemom culture production. This project then opens into an analysis of how to actually study digital feminist practices in this current moment, one that is defined by methodological crises surrounding the increasing complexities of enacting justice in social media research. This paper thus serves as a methodological disquisition for feminist researchers attempting to perform ethically just social media research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it